Leveraging AI Governance as a Competitive Advantage

In today's rapidly evolving technological landscape, Artificial Intelligence (AI) has become a cornerstone for innovation and growth across various industries. From enhancing customer experiences to optimizing operations, AI offers tremendous potential. However, alongside its potential benefits come significant challenges, particularly in terms of governance and risk management. In this blog post, we'll delve into the critical importance of AI governance as a competitive advantage, using real-world examples to highlight the consequences of inadequate governance.

As Navrina Singh, Founder and CEO of Credo AI, said at the Cyara XChange event in 2024: 

"Scalability can't occur without governance. No governance leads to not having control over your AI platforms. A tiny error can lead to total distrust, and trust is key." 

This statement encapsulates the essence of AI governance. Without proper governance frameworks in place, organizations risk losing control over their AI systems, which can have far-reaching consequences.

The Google Debacle: Bard Hallucination

Let's start by examining a cautionary tale from Google, where a lack of governance led to a substantial financial setback. In 2023, Google experienced a significant drop in its stock value, losing a staggering $100 billion (equivalent to an 8% drop in stock share price) in a single day. The root cause? A Google Bard, now called Gemini, “hallucination” – a term coined to describe a scenario where an AI system makes erroneous statements based on flawed data interpretation.

In this case, Bard claimed that the James Webb Space Telescope had captured the world’s first images of a planet outside our solar system, an assertion that was deemed false afterwards. The incident highlighted the critical need for robust AI governance mechanisms to prevent such costly errors. Had Google implemented stricter governance protocols, such as rigorous data validation and human oversight of AI-driven decisions, the impact could have been mitigated or avoided altogether.

The Google example underscores the importance of trust in AI systems. Trust is not just a buzzword; it's a fundamental pillar that underpins successful AI deployment. Without trust, users, customers, and investors may lose confidence in AI-driven solutions, hampering adoption and limiting the potential benefits that AI can offer.

Real-World Impact

Irresponsible AI can lead to widespread misinformation, which directly impacts the knowledge society retrieves from these systems. The effects can be seen on any industry in which AI tools are used. The examples are numerous: healthcare misdiagnosis, inaccurate journalism, deepfakes, incorrect financial advisory, and so on.

Looking beyond these implications, AI governance also plays a pivotal role in addressing ethical considerations and societal impact. A recent Gartner report warned of the potential dangers posed by Generative AI. The report projected that without adequate governance and ethical frameworks, there's a real risk of AI systems causing harm, including the possibility of fatalities by 2027.

This sobering prediction serves as a wake-up call for organizations to prioritize AI governance as part of their strategic initiatives. By proactively addressing governance challenges, companies can not only mitigate risks but also gain a competitive advantage in several ways:

  1. Enhanced Trust and Reputation: Implementing robust AI governance instills trust among stakeholders, including customers, investors, and regulatory bodies. A reputation for responsible AI practices can differentiate an organization in the market and attract partners and customers who prioritize ethical considerations.
  2. Risk Mitigation and Compliance: Effective governance frameworks help identify and mitigate risks associated with AI deployment, ensuring compliance with regulatory requirements and industry standards. This proactive approach minimizes the likelihood of costly legal disputes or regulatory fines.
  3. Innovation Acceleration: Contrary to popular belief, stringent governance doesn't stifle innovation; instead, it fosters responsible and sustainable innovation. By aligning AI initiatives with ethical guidelines and risk management protocols, organizations can confidently explore new use cases and unlock opportunities for growth.
  4. Operational Efficiency: Well-defined governance structures streamline AI development, deployment, and monitoring processes, leading to improved operational efficiency and resource allocation. This efficiency gains a competitive edge by delivering value faster and more reliably than competitors with less mature governance practices.

How to Deploy AI Governance Effectively

The AI governance journey is not static. It requires continuous development and revision to support new and existing AI systems, data sources, and stakeholders to allow tailored governance practices to your company’s specific use cases. The illustration below depicts some actionable steps to guide you deploy AI governance in an effective manner.

Conclusion

AI governance is not just a regulatory necessity but a strategic imperative for organizations seeking to harness AI's full potential. The Google stock market incident and the Gartner report serve as stark reminders of the consequences of overlooking governance considerations. By prioritizing governance, organizations can turn potential risks into competitive advantages, paving the way for responsible AI-driven innovation and long-term success. 

For any further information you or your company may need on Artificial Intelligence, do not hesitate to contact us at [email protected]. We help our clients develop a comprehensive understanding of AI capabilities and applications. Whether you're looking to integrate AI into your existing systems, explore advanced AI solutions, or navigate ethical considerations in AI deployment, our team is committed to providing tailored support to drive your success.

 

Is Your Organization Ready for AI Adoption?

Artificial intelligence (AI) is rapidly transforming industries, offering exciting opportunities to optimize processes, gain valuable insights, and unlock new levels of efficiency. However, navigating the path to successful AI adoption can be daunting. This blog is designed to guide business leaders through a crucial first step: assessing your organization's AI readiness. By understanding your strengths and areas for improvement, you can make informed decisions and position your company for success in the AI age.

Beyond the Hype: Defining Your AI Vision

Before diving headfirst into AI implementation, it's fundamental to establish a clear vision and alignment with your overall business strategy. This involves asking critical questions:

Open communication and a shared understanding of the "why" behind AI adoption are crucial for fostering long-term success.

Building a Strong Foundation: Key Considerations

Beyond the vision, there are several key areas to consider when assessing your organization's AI readiness. While the specific requirements will be unique to your organization and your solutions, engaging with a trusted partner can provide a comprehensive evaluation of the following:

Addressing these fundamental aspects is essential for building a solid foundation for your AI journey.

Taking the Next Step: Partnering for Success

Navigating the complexities of AI adoption requires expertise, experience, and a tailored approach. Partnering with Kenway Consulting’s AI Practice can provide the guidance and support you need to:

By taking a proactive approach and engaging with a trusted partner, you can confidently embark on your AI journey, unlock its transformative potential, and gain a competitive edge in the ever-evolving business landscape.

Embrace the Future with Confidence: Partnering for AI Success

In the dynamic world of business, staying ahead of the curve is crucial. Embracing AI presents a unique opportunity to optimize, innovate, and gain a significant competitive advantage. However, embarking on this journey requires careful planning, a solid foundation, and the right guidance.

Partnering with a reputable consulting firm provides a valuable advantage, offering expert insights, tailored strategies, and the support required to navigate your AI journey successfully. By collaborating with experienced professionals, you can confidently embrace the transformative power of AI, achieve your organizational goals, and unlock a future of intelligent success.

Ready to unlock the power of AI in your organization? Contact us today and let's discuss how we can help you achieve your goals.

FAQs:

It depends! An AI Readiness Assessment can help identify your organization's strengths and weaknesses for AI adoption. Strong data and a culture of innovation are key factors.

AI adoption often follows a phased approach: Explore AI's potential, assess your readiness, run a pilot project, scale successful solutions, and continuously improve.

AI can boost efficiency by automating tasks, unlock valuable insights from data, fuel innovation, and elevate your customer experience.

               

              Unlocking Efficiency: Generative AI's Role in Contact Center Innovation

              Introduction

              Throughout the past year, Kenway Consultants have been deeply involved in orchestrating the buildout of a Contact Center as a Service (CCaaS) implementation at a major telecom provider in the United States. Our team has played critical roles on the program such as defining the customer experience for each unique self-service offering and expanding on the functionality of their existing IVA. Kenway continues to successfully bridge the gap to the business while working closely with AI Architects, Developers, and technical teams. In this blog, we will share our insights, key challenges faced during the implementation of Generative AI, and the steps taken to overcome these obstacles.

              In 2023, more than 25% of all investment dollars in American startups were channeled into AI-focused companies. Global spending on AI for 2024 is projected to exceed $110 billion. Much of this investment is geared towards Generative AI, which saw unprecedented innovation in the last year. Implementation in the real world has spanned across industries, including Technology, Media, Telecom, and Financial Services, due to the clear alignment of use cases in those industries.

              The Time-Intensive Problem

              Before diving into the insights gained by implementing Generative AI in the Contact Center space, we will expand on some of the problems with traditional prompt creation and routing to further emphasize the importance of Generative AI. The first piece is the time intensive nature of the traditional flow or prompt creation. Before Developers can build a self-service experience, a myriad of requirements needs to be discussed and documented. Business Analysts define the requirements, conversational architects will take those requirements and build a visual flow representing the experience, content writers adjust and approve all language that a customer will hear, technical teams conduct data mapping to configure the technical solution, and so on. 

              Despite this level of attention to detail, certain use cases and edge cases will fall through the gaps and will only be discovered once the experience is live in production. This feeds into the second piece that all callers hear the same generically constructed content, offering a less personal, more robotic experience. Furthermore, customer utterances deemed a “No-Match”, or a “No-Input” have no path forward other than endless retries and ultimately speaking to a live agent. Traditional implementation fails to capture these use cases, as it is extremely time-consuming, and expensive, to build handling and routing for all potential utterances a real customer may provide. 

              Generative AI in Contact Centers

              The introduction of Generative AI helps ease this pain on all fronts. Leveraging Gen AI solutions can reduce time spent throughout the Software Development Lifecycle (SDLC) by reducing the number of prompts and routing needed to deliver a particular enhancement and reducing the time spent solutioning edge cases. Technical teams can spend their time more efficiently, and superior experiences are also provided to the customer. Generative AI enables companies to provide personalized prompting for each individual caller’s needs. With the ability to leverage content from the companies’ website, existing forums, corporate databases, and more – Generative AI can offer dynamic informational prompting once trained on these materials. 

              Secondly, Generative AI can mimic the conversational style of the caller as it starts to use words similar to the caller’s verbiage as the call goes on, repeating it back if the system is having trouble matching the customers’ utterance to an established route or intent. This powerful feature emulates a human-to-human connection, allowing the IVA to respond as a human would. 

              Finally, the IVA can keep callers engaged longer with the use of Generative AI, which ultimately improves containment rates, a key metric used to gauge the performance of self-service experiences. In short, an IVA containment rate refers to the percentage of inbound calls or chats that are successfully handled without having to speak to a human agent. Higher containment rates equate to a lower volume of live agent transfers, improved agent workloads, and in parallel, a potential reduction in labor costs for the organization.

              Limitations and Challenges of Generative AI Implementation

              While acknowledging the benefits, the use of Generative AI poses potential drawbacks and challenges. As programs navigate from the traditional SDLC to leveraging Generative AI, the first concern is that testing (Quality Assurance, IST, and User Acceptance Testing) can be significantly more cumbersome. Generative AI provides dynamic prompting to different callers, this requires more test cases to verify the application is functioning correctly. To alleviate this stress, organizations can use automated testing platforms such as Botium, by Cyara, to test AI with AI. A powerful tool such as Botium can go through thousands of test cases in a matter of minutes. What better way to test AI, than with AI?

              In the AI world, made-up values or incorrect facts are called hallucinations. These hallucinations can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. Due to this, there are concerns that stem from a legal and content standpoint. Generative AI may lead to the usage of verbiage or language that does not necessarily have signoff from all involved parties. In the traditional model, content writers meticulously groom each user facing prompt with legal considerations top of mind. Due to the dynamic model of Generative AI, customers may receive content that misrepresents the business and their normal communication standards. Even more concerning, there is the risk that something being communicated is inaccurate or misleading to the user, potentially opening the organization up to legal penalties. For instance, a recent article by Forbes discusses the legal ramifications of these hallucinations within the airline industry. Additionally, the Guardian outlines a recent use case in the court of law that resulted in severe repercussions for a Canadian lawyer.

              Mitigation through Design

              However, there are tools to mitigate these risks. The first being the appropriate selection of the Large Language Model (LLM), or AI toolset, and the data used to train the model. Establishing robust guidelines during the model training timeframe can help limit this risk downstream. The data upon which the model is trained will need to be selected carefully to avoid bias. It is important to also continuously tune and deploy the model with parameters that strike a balance between generating a human-like customer experience and one that does not stray from the confines of its knowledge base (i.e. creating hallucinations). The perpetual monitoring of the model’s performance after it has been deployed to production will further refine the output.

              Secondly, leveraging automated testing tools to cover thousands of use cases and edge cases will allow for teams to identify defects further up in the DevOps lifecycle, before those defects reach production, reducing costs down the road. 

              Lastly, keeping the concept of Responsible AI at the forefront when designing the model guidelines will ensure these risks are further tamed. It is worth noting that the selection process for Generative AI will vary depending on the business or industry it intends to support. For example, the AI model deployed in a Contact Center at a telecom provider will vary significantly from a model used to serve an internal purpose at a Private Equity firm, or one used to send out targeted marketing campaigns to potential customers. These models can be seen as highly customizable, where the concept of ‘one size fits all’ does not apply.

              Above all, the most impactful mitigation tool for AI risks is choosing the right use cases. Specifying all-encompassing use cases, creating KPIs around engagement volume, evaluating the potential severity of defects, and where the highest ROI would come from utilizing generative AI, can all be very difficult. However, by selecting appropriate use cases, most generative AI limitations can be avoided based on these decisions being made further upfront in the design process.

              Moving Forward with AI in Contact Centers

              It is important to reiterate that the model must have access to reliable and germane data on which it can be trained. Think of a chatbot trained on semi-biased data, potentially perpetuating stereotypes or generating offensive hallucinations. Acquiring and curating high-quality, contextually relevant data is paramount. No business function will be successful without the iterative process of testing, investigating, enhancing, and reassessing the performance of Generative or Conversational AI. This iterative approach ensures that these models not only function effectively, but also continually adapt and grow, becoming invaluable assets for businesses with aims to stay ahead of the curve. 

              AI is continuing to evolve and advance, the future of Generative AI is a bright, and an ever-changing horizon. At Kenway, we have built expertise over the last few years in the AI realm, and we have launched our newly formed Artificial Intelligence Practice. Our services include: 

              As Kenway takes on more Contact Center Implementations and seeks to expand into other industries that are now ready to realize the ROI of AI, the firm continues to design intelligently for the future, ensuring the latest methodologies and tools are deployed at our clients. To read more about our Contact Center Practice, please visit: Contact Center Solutions.

              FAQs: 

              How important is data to being able to build an AI tool?

              The need for quality and integrity of data underpinning an AI tool cannot be overstated. Across all industries, AI, especially Generative AI, requires a robust, compliant, application-specific data set without bias to produce high-quality, ROI-driving responses regardless of the specific application. Selecting the right data set can be the most important decision a firm makes when making implementation decisions.

              What guidelines or frameworks should I consider when looking to implement Generative AI in my organization?

              Our business is doing great, and we are consistently beating our revenue targets. Why do I need to invest in AI?

              Making a data-driven decision on the implementation of artificial intelligence for a specific use-case can not only reduce time spent on specific workstreams, but even open doors to new ways to interact with customers, more efficient ways to manage tasks, and ensure your organization stays ahead of the curve.

              How do I scale?

              Before delivering an AI solution at scale across an enterprise, ensure the following criteria are met. The model must be maintainable, robust with the ability to avoid hallucinations, and executed efficiently throughout the continuous deployment cycle. The model should have the ability to integrate with core systems and provide performance monitoring to avoid any detrimental impacts on internal operations.

                           

                          Building Your Bridge to AI: Architecting a Tech Stack for Success

                          Artificial Intelligence (AI) is no longer a futuristic concept, but a tangible reality transforming businesses across industries. From optimizing operations to enhancing customer experiences, AI promises significant value creation. However, the development of robust technical architectures, including an AI tech stack, is imperative for the success of AI solutions, from data to deployment.

                          At Kenway Consulting's AI Practice, we believe a successful AI implementation hinges on a strategically designed AI tech stack that aligns with your specific business objectives and data landscape. This comprehensive guide delves into the intricacies of building a solid foundation, covering the key aspects of data collection and storage, data processing and infrastructure, model selection and training, vector stores, model deployment, monitoring, and practical applications of AI models. Additionally, we emphasize the responsible use of AI, acknowledging the ethical considerations that should guide the development and deployment of these powerful technologies.

                          Data Collection and Storage

                          The cornerstone of any AI solution lies in meticulous data collection, labeling, and storage practices. The first step involves creating a seamless data collection process, often facilitated by custom tools. To streamline this process, developing a data labeling tool can empower users to label and organize datasets efficiently.

                          At the core of your AI tech stack lies the data infrastructure. This encompasses platforms for secure and scalable data storage, both raw and processed. Scalable storage solutions are vital to handle the massive volumes of data generated in AI projects. Consider cloud-based options like AWS S3 or Azure Blob Storage for their flexibility and cost-efficiency. Alongside, a data lake acts as a repository for raw data, while a data warehouse stores structured data for model training and serving. 

                          Data Infrastructure and Processing

                          To connect the data collection and storage elements, data pipelines automate the flow of data, ensuring its cleanliness and readiness for analysis. Employing movement tools, warehousing, and preprocessing pipelines is crucial for efficient data processing. Automation and transformation tools, coupled with synthetic data generation, play a pivotal role in enhancing the quality and diversity of datasets.

                          Building a data preprocessing pipeline using popular tools can streamline the data preparation process. Automating data movement, cleaning, and transformation processes within a data pipeline accelerates the entire workflow. Leveraging tools like Snowflake as a data warehouse for storage solutions enables seamless integration with generative AI models.

                          The next cornerstone is the compute infrastructure. Whether you choose cloud platforms like AWS EC2 or Azure VMs for their dynamic scalability or opt for on-premises high-performance computing clusters for intensive tasks, ensure your resources can handle the demands of your AI workload. For computationally intensive tasks like training large models, consider leveraging GPU accelerators to boost processing power.

                          Model Selection and Training

                          Now comes the heart of the matter: model development and management. Choosing the right model is a critical decision in AI implementation and development. Your organization will be faced with a choice between foundational and fine-tuned custom models. Large Language Models (LLMs) offer a spectrum of options for businesses, ranging from out-of-the-box solutions from OpenAI and Google, which provide robust pre-trained models suitable for various tasks albeit with associated costs, to open-source alternatives such as Meta’s Llama models, offering cost-effective yet customizable options. 

                          For organizations with unique needs or proprietary data, training a custom LLM tailored to their domain-specific requirements becomes a compelling choice, despite the resource-intensive nature of this approach. It enables incorporation of internal knowledge and sector-specific nuances, optimizing model performance for specialized tasks. Alternatively, techniques like RAG (Retrieval-Augmented Generation) can augment your model's knowledge base without any custom training. Each option presents distinct trade-offs in terms of convenience, cost, and customization, empowering businesses to select the most suitable strategy based on their priorities and resources.

                          Evaluating popular machine learning frameworks and utilizing high-performance computing resources, including GPUs and TPUs, can significantly impact training performance. Choose frameworks like TensorFlow or PyTorch to build and train a custom AI model. To ensure transparency and reproducibility, implement a model registry for version control and tracking. 

                          Vector Stores

                          Vectors are mathematical entities representing both magnitude and direction, commonly used in physics and mathematics. In AI, vectors serve as numerical representations of various data points, such as words, sentences, or images, facilitating computations and comparisons within machine learning models. To manage these vectors efficiently, AI systems often rely on specialized storage spaces known as vector stores. These repositories store and organize vectors, enabling quick access and retrieval for AI algorithms and language models, optimizing their performance and efficiency.

                          Vector stores play a vital role in handling vectorized data efficiently. Exploring options for vector stores and their applications in storing and retrieving vectorized data adds another layer to the technical architecture. For instance, during natural language processing tasks such as sentiment analysis or document classification, the vector store enables rapid lookup and comparison of embeddings, enhancing the speed and accuracy of model inference.

                          Model Deployment

                          Once trained, models need to be deployed seamlessly into production environments. The deployment phase involves critical decisions such as platform selection and containerization. Utilizing platforms like Docker for containerization and ensuring model serving, monitoring, and consistent performance are essential considerations. Containerizing a trained model and deploying it on a cloud platform exemplifies an effective deployment solution.

                          Setting up a model serving and monitoring system, coupled with real-time performance monitoring and adjustments, ensures that the deployed AI models operate seamlessly. Compare simplified deployment options on cloud platforms like Azure, AWS, and Google before choosing the right platform for your specific project requirements.

                          Finally, models need to be accessible to applications through APIs or web services. This enables you to build powerful AI applications with your model.

                          Monitoring and Security

                          Your AI journey doesn't end with deployment. Monitoring and observability are essential for ensuring model performance and system health. Implement robust metrics and logging systems to track model accuracy, resource utilization, and potential anomalies. Set up alerts to notify you of any performance degradation or errors, allowing for proactive troubleshooting. Data visualization tools help you understand model behavior and identify areas for improvement.

                          Implementing robust data security measures to protect sensitive information is a crucial aspect of AI implementation. Ensure your models are not susceptible to adversarial attacks or biased outputs. Establish clear processes for model approval, deployment, and lifecycle management, ensuring alignment with ethical and regulatory guidelines.

                          Responsible AI Usage

                          In the pursuit of technological advancement, it is paramount to address the ethical considerations surrounding AI development and deployment. Responsible AI usage involves ensuring fairness, transparency, and accountability throughout the entire lifecycle of an AI solution. Developers must be mindful of potential biases in training data and models, striving to create systems that are inclusive and do not perpetuate or exacerbate existing inequalities.

                          Moreover, incorporating privacy-preserving measures and obtaining informed consent from users are essential steps in the responsible usage of AI. The transparency of AI systems, providing clear explanations for their decisions, fosters trust among users and stakeholders. As AI continues to shape our world, it is incumbent upon developers and organizations to prioritize responsible AI practices, aligning technological progress with ethical considerations.

                          Beyond the Technical: Orchestrating the Process

                          The success of your AI initiative also depends on organizational considerations. Decide on a centralized or decentralized approach for managing AI projects and resources. Build cross-functional teams with expertise in data science, engineering, operations, and business domains. Invest in training and education programs to equip your workforce with the necessary skills for working with AI. Additionally, explore infrastructure as code (IaC) tools like Terraform for automating infrastructure provisioning and management.

                          AI Applications

                          AI holds immense potential for businesses across various domains. Through its capabilities in content generation, it can efficiently produce a wide array of materials, from articles to product descriptions, reducing the burden on human resources while ensuring a consistent output quality. Leveraging an AI-powered chatbot for training or human resources functions can streamline processes such as onboarding, employee support, and performance evaluations, enhancing efficiency and accessibility within the organization. In creative design, AI-driven tools can offer fresh perspectives and innovative designs for branding materials, websites, and marketing campaigns, helping businesses maintain a visually appealing and engaging presence in the market. Overall, by harnessing AI, businesses can optimize operations, foster creativity, and gain valuable insights, ultimately driving innovation and competitiveness in today's dynamic market landscape.

                          Embrace the Potential of AI with the Right AI Tech Stack

                          Building a successful AI foundation requires careful planning, a robust AI tech stack, and a collaborative approach. By following these architectural principles and leveraging the expertise of trusted partners like Kenway Consulting, you can unlock the transformative power of AI and ensure successful AI implementation, unlocking new possibilities for your business.

                          Choosing the right AI tech stack for your AI ambitions is not a one-size-fits-all approach. Carefully consider your organization's size, complexity, and specific AI applications. At Kenway Consulting, we understand the unique challenges of implementing AI, and we partner with you to define your AI vision, design a tailored AI tech stack, and guide you through every stage of the journey.

                          Ready to start your AI journey? Contact us today and let's build your bridge to success together.

                          FAQs:

                          A tech stack refers to the combination of programming languages, frameworks, libraries, and tools utilized to build and run a software application or website. It encompasses both front-end and back-end technologies. 

                          Choosing the best tech stack involves considering factors such as project requirements, team expertise, community support, scalability, cost, performance, security, integration, and compatibility. Assessing the specific needs of your project, the skills of your team, and the availability of resources and support for different technologies is crucial. 

                          To build your tech stack, start by defining the requirements and goals of your project. Based on these, research and select the appropriate technologies for both the front end and back end, considering factors such as scalability, performance, security, and compatibility. Assess your team's expertise and familiarity with different technologies to ensure efficient development. Create a cohesive architecture by integrating chosen components and tools, ensuring they work seamlessly together. Regularly evaluate and update your tech stack to incorporate new technologies, address emerging requirements, and optimize performance.

                           

                          Databricks Data + AI World Tour 2023: Conference Highlights

                          At Kenway Consulting, we specialize in helping our clients with modern data enablement and unified data for analytics. One of the technology solutions we endorse for data product teams is Databricks. 

                          For those not yet familiar with Databricks, it is a full stack and cloud-based platform that supports data engineering, data analytics, data science, and machine learning. Ultimately, it enables data teams to easily collaborate by auto-scaling compute resources leveraging interactive notebooks to run SQL/code and immediately visualize data in a single document.

                          Insights from Databricks Data + AI World Tour 2023

                          To keep up with recent trends and evolving capabilities, we recently attended a Databricks Conference called Databricks Data + AI World Tour in Chicago on October 4, 2023. Databricks left us awestruck with their relentless pace of innovation in data and AI. The advancements in metadata driven development (data ingestion) using Unity Catalog show their commitment to making data easily accessible and governed across the organization. 

                          By integrating natural language processing models into workflows through Koalas, they are bridging the gap between business users and complex AI. The continuous improvements in simplifying machine learning development and deployment through MLflow highlight Databricks' leadership in MLOps. Their upcoming Salesforce integrations. will unleash new possibilities for customers.

                          Streamlining Data Governance for Innovation

                          Ultimately, by making machine learning, MLOps, and data governance frictionless through products like MLflow, Koalas, and Unity Catalog, Databricks is freeing companies from the drudgery of data wrangling. Instead, we can now focus our energy on running experiments with predictive analytics to create asymmetric value. The brilliance of Databricks lies in empowering us to swiftly turn raw data into extraordinary insights.

                          We left buzzing with excitement about incorporating these cutting-edge capabilities into our own data projects. Databricks has ignited our appetite for innovation, and we eagerly anticipate their next groundbreaking developments through their Data AI Summit. They have proven themselves trailblazers in data and AI, and we are thrilled to be on this journey with them.

                           

                          Exploring AI Applications: So Easy a Human Can Do It

                          Artificial Intelligence (AI) is regarded as a complex and mystifying concept, seemingly beyond the grasp of any person not in possession of an advanced degree in mathematics. Contrary to popular belief, AI is becoming increasingly accessible and user-friendly, empowering individuals without technical backgrounds to harness its power. In this blog, we will explore how access to AI has been expanded, AI applications that are now available to non-technical users, the rise of low-code/no-code platforms, and the ethical considerations surrounding this democratization of AI.

                          Tracing the Growth of AI

                          From Complex Algorithms to User-Friendly Intelligence

                          To begin, it is important to understand what AI truly encompasses. AI refers to the development of intelligent systems that can perform tasks that typically require human intelligence. While AI may involve complex Machine Learning (ML) algorithms and advanced statistical techniques, the technology itself has evolved to become more user-friendly and approachable. Gone are the days when AI was solely the domain of computer scientists and data analysts.

                          AI has permeated various aspects of our daily lives, and many of us interact with it without even realizing it. Personal AI assistants that utilize Natural Language Processing (NLP) like Siri, Alexa, and Google Assistant have become commonplace, making tasks such as setting reminders, answering questions, and playing music as easy as speaking a few words. Social media algorithms utilize AI to personalize our news feeds and recommend content that aligns with our interests. E-commerce platforms apply ML to employ AI algorithms to suggest products and services tailored to our preferences. Furthermore, AI-powered content creation tools enable non-experts to produce professional-level designs, write engaging content, and even edit videos effortlessly.

                          Empowering AI Applications with Low-Code and No-Code Platforms

                          The democratization of AI has been further accelerated by the rise of low-code and no-code platforms. These platforms allow individuals with minimal coding knowledge to build and deploy AI applications, using ML, NLP, robotics, computer vision, or any number of capabilities. By providing intuitive interfaces and drag-and-drop functionalities, low-code/no-code platforms eliminate the need for extensive programming expertise. This democratization opens a world of possibilities, empowering non-technical users to create their own AI and ML models, reducing reliance on technical experts, and fostering innovation across industries.

                          AI and Data Privacy

                          As AI becomes more accessible, it is crucial to address the challenges and ethical considerations that accompany its widespread use. One key concern is bias and fairness in AI algorithms. To ensure equitable outcomes, efforts must be made to identify and mitigate biases that may be present in training data. Data privacy and security also become paramount as AI relies on vast amounts of personal information. Transparency and accountability are equally important, with a need to understand how AI models make decisions and ensure responsibility for their outcomes.

                          It is important to recognize that AI applications are not intended to replace humans but rather to augment human capabilities. Instead of fearing the rise of AI, we should embrace it as a powerful tool that can enhance our lives. By understanding AI and its applications, individuals can leverage its potential to solve complex problems, improve efficiency, and unlock new opportunities. Collaboration between humans and AI applications can lead to transformative advancements across various fields, enabling us to tackle grand challenges with greater precision and speed.

                          How Kenway Can Help

                          At Kenway we’re learning to embrace this new technology in a variety of ways to help us mature and grow as an organization. We’re using NLP via Microsoft Azure’s OpenAI Studio to summarize and catalog the blogs and case studies on our website. Doing so automates a process that enables our internal marketing team members to provide useful insights to the business development team. These insights are employed to make informed and strategic decisions about growing our business in the most optimal and efficient ways possible.

                          Additionally, in conjunction with Kenway’s Contact Center practice, our Artificial Intelligence practice is in the process of leveraging Google Cloud’s Generative AI platform to develop an employee-facing chatbot. The chatbot incorporates NLP to answer common questions from employees about internal procedures and policies based on the Kenway employee handbook. The leaders of Kenway’s finance department are thrilled for a future where they can direct coworkers to the chatbot at the end of every month when questions about expense logging typically overwhelm.

                          AI Applications

                          The once-daunting concept of AI has been transformed into a user-friendly and accessible technology, enabling individuals from all walks of life to tap into its power. From personal assistants and social media algorithms to low-code/no-code platforms and ethical considerations, AI has evolved to be so easy that a human can indeed do it. As we embrace AI applications and continue to innovate, it is essential to strike a balance between harnessing the capabilities and upholding ethical standards. Let us embrace this transformative technology and leverage its potential to shape a better future for all.

                          Just getting started with AI? Questions about how to take the first step? Connect with us at [email protected].